GLM-5.1 vs LFM2.5-VL-450M

Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.

GLM-5.1

79

VS

LFM2.5-VL-450M

33

1 categoriesvs0 categories

Pick GLM-5.1 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

Category Radar

Head-to-Head by Category

Category Breakdown

Knowledge

GLM-5.1
52.3vs21.6

+30.7 difference

Operational Comparison

GLM-5.1

LFM2.5-VL-450M

Price (per 1M tokens)

$1.4 / $4.4

$0 / $0

Speed

N/A

N/A

Latency (TTFT)

N/A

N/A

Context Window

203K

128K

Quick Verdict

Pick GLM-5.1 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.

GLM-5.1 is clearly ahead on the provisional aggregate, 79 to 33. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GLM-5.1's sharpest advantage is in knowledge, where it averages 52.3 against 21.6.

GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-VL-450M. That is roughly Infinityx on output cost alone. GLM-5.1 is the reasoning model in the pair, while LFM2.5-VL-450M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GLM-5.1 gives you the larger context window at 203K, compared with 128K for LFM2.5-VL-450M.

Benchmark Deep Dive

Frequently Asked Questions (2)

Which is better, GLM-5.1 or LFM2.5-VL-450M?

GLM-5.1 is ahead on BenchLM's provisional leaderboard, 79 to 33.

Which is better for knowledge tasks, GLM-5.1 or LFM2.5-VL-450M?

GLM-5.1 has the edge for knowledge tasks in this comparison, averaging 52.3 versus 21.6. LFM2.5-VL-450M stays close enough that the answer can still flip depending on your workload.

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Last updated: April 8, 2026

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